[1]} use the term “implicit copula” for the
copula that is implicit in the multivariate distribution of a continuous
random vector \(\mathbf {Z}=(Z_1,\ldots ,Z_m)^\top \) . It is obtained by inverting Sklar's theorem, which [2]} calls the “inversion
method”, so that copulas derived in this fashion are also called “inversion copulas” (e.g. [3]}).
If \(\mathbf {Z}\)  has distribution function \(F_Z\)  with marginals \(F_{Z_1},\ldots ,F_{Z_m}\) , then its implicit copula function is
\(C_Z(\text{$u$})=F_Z\left(F_{Z_1}^{-1}(u_1),\ldots ,F_{Z_m}^{-1}(u_m)\right)\,.\) 
